【摘要】 在E-learning环境中,为了满足用户对学习资源的个性化需求,提出了一种基于语义网技术的学习资源个性化推荐算法。首先根据用户评价和浏览行为得到用户感兴趣的学习资源集合与核心概念集合,然后根据领域本体中概念间的关系分别计算不同用户评价的学习资源集合间的语义相似度和核心概念集合间的语义相似度,最后根据得出的两个相似度值共同决定用户兴趣偏好的相似性,找到具有相似兴趣的最近邻居,从而实现学习资源的协同推荐。此外,在学习资源管理上引入了学习对象概念,降低了相似度计算的复杂度。并将该算法应用到了基于语义网的个性化学习资源推荐系统中,实验表明,该算法有效改善了学习资源推荐效果,特别是对于新加入的资源和新注册用户效果显著。更多还原

【Abstract】 In E-learning,in order to satisfy users’ personalized demands for learning resources,in this paper we present a learning resources personalized recommendation algorithm based on semantic web technology.First of all,based on users’ evaluations and browsing behaviors of the learning resources,we define the learning resource set and core concept set for a user;secondly,based on the relations of concepts in domain ontology,we obtain the user’s preference similarity by computing semantic similarity between the different users’ learning resource sets and core concept sets respectively.At last,we recommend collaboratively the learning resources according to the user’s preference similarity.In addition,the learning objects for management of learning resources can reduce the complexity of similarity computation.We applied this algorithm to the personalized learning resources recommendation system,the experiments show that the algorithm improved the effectiveness of the recommended learning resources,especially for new resources and new registered users.更多还原